METHODS AND SYSTEMS FOR MANAGING A RESOURCE IN A NETWORKED STORAGE ENVIRONMENT

• US 20170026312A1
• Filed: 07/22/2015
• Published: 01/26/2017
• Est. Priority Date: 07/22/2015
• Status: Active Grant

• Current Assignee: NetApp, Inc.
• Original Assignee: NetApp, Inc.
• Inventors: MacFarland, Jeffrey Scott, Hrischuk, Curtis, Dimnaku, Alma
• Granted Patent: US 10,397,324 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: H04L 67/1097 for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]

Abstract

Methods and systems for a networked storage system are provided. One method includes receiving a resource identifier identifying a resource of a network storage environment as an input to a processor executable application programming interface (API); and predicting available performance capacity of the resource by using an optimum utilization of the resource, a current utilization and a predicted utilization based on impact of a workload change at the resource, where the optimum utilization is an indicator of resource utilization beyond which throughput gains for a workload is smaller than increase in latency in processing the workload.

Claims

1. A machine implemented method, comprising:

receiving a resource identifier identifying a resource of a network storage environment as an input to a processor executable application programming interface (API); and

predicting available performance capacity of the resource by using an optimum utilization of the resource, a current utilization and a predicted utilization based on impact of a workload change at the resource, where the optimum utilization is an indicator of resource utilization beyond which throughput gains for a workload is smaller than increase in latency in processing the workload.

2. The machine implemented method of claim 1, wherein the resource is a processor for a networking module that provides networking functionality in the networked storage environment, a processor for a storage module that interfaces with the storage device for storing data and an aggregate that includes the storage device.

3. The machine implemented method of claim 1, wherein the optimum utilization is determined by using a plurality of techniques that generate a relationship between latency and utilization of the resource.

4. The machine implemented method of claim 1, wherein when the resource is a storage system node that is configured as a failover node for another peer node, then utilization of both the storage system node and the failover node is used to predict the available performance capacity of the resource.

5. The machine implemented method of claim 1, wherein an operational point for determining the available performance capacity is based on a defined service level latency for the resource, which is provided as an input for the API.

6. A non-transitory, machine readable storage medium having stored thereon instructions for performing a method, comprising machine executable code which when executed by at least one machine, causes the machine to:

receive a resource identifier identifying a resource of a network storage environment as an input to a processor executable application programming interface (API); and

predict available performance capacity of the resource by using an optimum utilization of the resource, a current utilization and a predicted utilization based on impact of a workload change at the resource, where the optimum utilization is an indicator of resource utilization beyond which throughput gains for a workload is smaller than increase in latency in processing the workload.

7. The storage medium of claim 6, wherein the resource is a processor for a networking module that provides networking functionality in the networked storage environment, a processor for a storage module that interfaces with the storage device for storing data and an aggregate that includes the storage device.

8. The storage medium of claim 6, wherein the optimum utilization is determined by using a plurality of techniques that generate a relationship between latency and utilization of the resource.

9. The storage medium of claim 6, wherein when the resource is a storage system node that is configured as a failover node for another peer node, then utilization of both the storage system node and the failover node is used to predict the available performance capacity of the resource.

10. The storage medium of claim 6, wherein an operational point for determining the available performance capacity is based on a defined service level latency for the resource, which is provided as an input for the API.

11. A system comprising:

a memory containing machine readable medium comprising machine executable code having stored thereon instructions; and

a processor module coupled to the memory, the processor module configured to execute the machine executable code to;

receive a resource identifier identifying a resource of a network storage environment as an input to a processor executable application programming interface (API); and

predict available performance capacity of the resource by using an optimum utilization of the resource, a current utilization and a predicted utilization based on impact of a workload change at the resource, where the optimum utilization is an indicator of resource utilization beyond which throughput gains for a workload is smaller than increase in latency in processing the workload.

12. The system of claim 11, wherein the resource is a processor for a networking module that provides networking functionality in the networked storage environment, a processor for a storage module that interfaces with the storage device for storing data and an aggregate that includes the storage device.

13. The system of claim 11, wherein the optimum utilization is determined by using a plurality of techniques that generate a relationship between latency and utilization of the resource.

14. The system of claim 11, wherein when the resource is a storage system node that is configured as a failover node for another peer node, then utilization of both the storage system node and the failover node is used to predict the available performance capacity of the resource.

15. The system of claim 11, wherein an operational point for determining the available performance capacity is based on a defined service level latency for the resource, which is provided as an input for the API.

16. A machine implemented method, comprising:

sending a request with a resource identifier identifying a resource of a network storage environment; and

receiving a response to the request including an available performance capacity of the resource, where the available performance capacity of the resource is determined by using an optimum utilization of the resource, a current utilization and a predicted utilization, where the optimum utilization is an indicator of resource utilization beyond which throughput gains for a workload is smaller than increase in latency in processing the workload.

17. The machine implemented method of claim 16, wherein the resource is a processor for a networking module that provides networking functionality in the networked storage environment, a processor for a storage module that interfaces with the storage device for storing data and an aggregate that includes the storage device.

18. The machine implemented method of claim 16, wherein the optimum utilization is determined by using a plurality of techniques that generate a relationship between latency and utilization of the resource.

19. The machine implemented method of claim 16, wherein when the resource is a storage system node that is configured as a failover node for another peer node, then utilization of both the storage system node and the failover node is used to predict the available performance capacity of the resource.

20. The machine implemented method of claim 16, wherein an operational point for determining the available performance capacity is based on a defined service level latency for the resource.